Geert Meyfroidt

发表

Prediction of Clinical Conditions after Coronary Bypass Surgery using Dynamic Data Analysis

Jean-Marie Aerts, Daniel Berckmans, Maurice Bruynooghe, 2010, Journal of Medical Systems.

Predictive data mining in intensive care

Maurice Bruynooghe, Greet Van den Berghe, Hendrik Blockeel, 2006 .

Best Practice & Research Clinical Anaesthesiology

Maurice Bruynooghe, Jan Ramon, Geert Meyfroidt, 2009 .

Mining data from intensive care patients

Maurice Bruynooghe, Jan Ramon, Hendrik Blockeel, 2007, Adv. Eng. Informatics.

Guided Monte Carlo Tree Search for Planning in Learned Environments

Maurice Bruynooghe, Jan Ramon, Greta Van den Berghe, 2013, ACML.

Machine learning methods for prediction in intensive care

Maurice Bruynooghe, Hendrik Blockeel, Daan Fierens, 2006 .

Dynamic Data Analysis and Data Mining for Prediction of Clinical Stability

Jean-Marie Aerts, Daniel Berckmans, Maurice Bruynooghe, 2009, MIE.

Real-time visualization of the pressure and time burden of ICP in individual patients

Greet Van den Berghe, Geert Meyfroidt, Bart Depreitere, 2016 .

Computerized prediction of intensive care unit discharge after cardiac surgery: development and validation of a Gaussian processes model

Jean-Marie Aerts, Daniel Berckmans, Maurice Bruynooghe, 2011, BMC Medical Informatics Decis. Mak..

Biomedical Signal Processing--Time Series Analysis-The Use of Multivariate Autoregressive Modelling for Analyzing Dynamical Physiological Responses of Individual Critically Ill Patients

Daniel Berckmans, Kristien Van Loon, Geert Meyfroidt, 2006 .

Time-series analysis techniques combined with Gaussian Process Classifiers for prediction of clinical stability after coronary bypass surgery

Jean-Marie Aerts, Daniel Berckmans, Maurice Bruynooghe, 2008 .

Dynamic Autoregressive Modelling of Critical Care Patients as a Basis for Health Monitoring

Jean-Marie Aerts, Daniel Berckmans, Greta Van den Berghe, 2012, BIOSIGNALS.

The Use of Multivariate Autoregressive Modelling for Analyzing Dynamical Physiological Responses of Individual Critically Ill Patients

Jean-Marie Aerts, Daniel Berckmans, Greta Van den Berghe, 2006, ISBMDA.

Impact of a computer-generated alert system on the quality of tight glycemic control

Greet Van den Berghe, Geert Meyfroidt, Dominiek Cottem, 2011, Intensive Care Medicine.

Intensive insulin therapy for patients in paediatric intensive care: a prospective, randomised controlled study

Greet Van den Berghe, Ilse Vanhorebeek, Geert Meyfroidt, 2009, The Lancet.

AKIpredictor, an online prognostic calculator for acute kidney injury in adult critically ill patients: development, validation and comparison to serum neutrophil gelatinase-associated lipocalin

Jan Gunst, Ilse Vanhorebeek, Geert Meyfroidt, 2017, Intensive Care Medicine.

Visualizing the pressure and time burden of intracranial hypertension in adult and paediatric traumatic brain injury

Ian Piper, Greet Van den Berghe, Rob Donald, 2015, Intensive Care Medicine.

Predicting blood temperature using Gaussian processes

Maurice Bruynooghe, Jan Ramon, Hendrik Blockeel, 2006 .

Feasibility of individualised severe traumatic brain injury management using an automated assessment of optimal cerebral perfusion pressure: the COGiTATE phase II study protocol

Ari Ercole, Marek Czosnyka, Geert Meyfroidt, 2019, BMJ Open.

How to implement information technology in the operating room and the intensive care unit.

Geert Meyfroidt, G. Meyfroidt, 2009, Best practice & research. Clinical anaesthesiology.

Predictive data mining on monitoring data from the intensive care unit

Geert Meyfroidt, Jelle Van Eyck, G. Meyfroidt, 2013, Journal of Clinical Monitoring and Computing.